Following the official guideline, we evaluate the KG embedding performance
by predicting new triplet edges according to the training edges.
The evaluation metric follows the standard filtered metric [1]}
which is widely used in KG.
Specifically, each test triplet edges are corrupted by replacing its head or tail with randomly-sampled 500 negative entities,
while ensuring the resulting triplets do not appear in KG.
The goal is to rank the true head (or tail) entities higher than the negative entities,
which is measured by Mean Reciprocal Rank (MRR).
